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Column – Smart IDMP: How to ensure that preparations for a “data-driven” future are a one-time investment


If there has ever been a case for doing things differently, and in a smarter, more coordinated way, it’s in preparation for the data-driven regulatory future. The EMA IDMP, which will be mandatory from February 2023, may be the flagship of this new reality, but it is not an end in itself. It is simply the first in a long series of digital requirements to emerge on a global scale, in all facets of the life sciences.

The FDA has long promoted similar goals related specifically to labeling and CMC information management in the pharmaceutical industry, with plans to expand its implementation of ISO data standards to other areas in due course, while that other regulators around the world are also considering something similar to IDMP. . At the same time, medical device regulations such as the EU’s MDR and identification standards (e.g. UDI) are advancing for medical device manufacturers – and who knows what else is looming on the horizon. ‘horizon.

The point is, we have a good idea of ​​where everything is heading, although many details are still evolving. What all of these emerging standards have in common is supporting new digitally enabled process efficiencies, beyond functional / company and even country boundaries, and for greater transparency and global accessibility of product information.

The mistake, if not the big missed opportunity, would be to invest in each set of regulatory requirements as a separate project. It would risk a significant expense, not to mention investing time and resources each time, only to create another system silo, a scenario that is at odds with the data-driven movement.

Smart money will therefore be spent to prepare not only for a given set of external demands, but for a data-driven future that will better serve internal operations and ultimately produce better patient outcomes.

But what does it look like and what’s the best way to go?

Reduce manual repetition

One way to look at this is to consider how many companies are still tackling regulatory applications and reporting. Typically this happens sequentially: documents are created, usually from scratch each time, through a careful process involving each department collecting their information before passing it on to the next team to add their input. .

EMA’s now clear target operating model for IDMP challenges this process head-on. This requires that the original data for each case is submitted / made available at the same time, which requires much closer synchronization and alignment of document / data management activity. This in turn suggests more continuous collaboration between teams from different departments to maintain a single, complete and current representation of the truth from which all submissions, knowledge and ideas flow.

IDMP shows the way, but is not the magic formula

While there was a temptation to wait until all the details of a particular regulatory mandate, including, but not exclusively, the EMA’s IDMP, were set in stone before making serious progress in transforming data-driven process, this is the wrong strategy.

On the one hand, the main objective of IDMP is to be a transfer protocol for the exchange of information between regulatory agencies and the life science industry, with the most immediate application in the ‘EU, and initially only for centralized procedures. In other words, its primary use case was never intended as an internal data repository model to help businesses run more optimally.

Thus, life science organizations can break free from their perceived dependence on external decisions to advance their own process transformations. They can use the principles and models from the EMA’s implementation of IDMP and other regulatory initiatives as useful models. But, given the diversity of the global regulatory landscape and the frequency with which requirements are updated, a more sustainable strategy will be to keep internal data models independent of regulator-specific parameters. Ideally, a company’s own data model will be built with the scope and flexibility to meet a wide and growing number of requirements through simple configuration. The key is that this new improved organization-wide capability will support the maintenance of up-to-date, high-quality data throughout the product lifecycle across all portfolios and all product variants, from here to eternity.

Invest for the long term

Most regulatory teams have understood the need to think sideways about compliance projects, but so far this has not necessarily translated into a more holistic and forward-looking approach to their preparations. More often than not, companies continue to postpone the inevitable until the deadlines feel right at hand and the specifics of each set of new regulatory requirements are known. At this point speed is everything and the temptation is to do something “quick and dirty” that gets the job done in the allotted time, no more, no less. Then when the requirements are updated or another region goes its own way, teams are forced to return to the drawing board.

The smart approach is different. It prioritizes preparation upstream, so that the company invests once and for all for all future eventualities. Instead of looking for a single, mandate-specific system that delivers what is needed for the latest demands of a region, it focuses on building a comprehensive internal capacity that captures and manages everything in a standard way for the organization. global. This master data and any related approved content can then be reused / reconfigured in different ways for each new set of needs.

Through collaboration across multiple departments and with support from the top of the organization, this emerging approach establishes a comprehensive internal data model with a high level of granularity, which can be continuously developed and adapted with input from across the board. business.

Planning for a sustainable future

There is still time to build a data-driven, future-proof system. regulatory information management (RIM) capacity, if businesses start now. The better the preparation, the more durable, reusable and valuable the resulting platform will be over time.

Here is how we recommend that you create such a capacity:

  1. Start by understanding the benefits of an ‘smart IDMP’ approach, or a versatile platform / capability, and an appreciation / acceptance that this will require more investment and planning now than a single goal compliance system. .
  2. Establish a baseline. Identify the relevant data the business has, and any gaps, as well as the steps and processes that will be needed to improve and maintain it, collaboratively and in the context of the desired data model. This needs to be seen both from an internal perspective and in the context of changing expectations of regulators – for example, the EU’s IDMP target operating model (which specifies the simultaneous delivery of records and data underlying, involving reduced reliance on manual copy and paste or cross-referencing between separate and parallel activities).
  3. Set up a project, define roles and responsibilities, and gain senior management support to drive any necessary business process transformation, and even organizational structure, to enable the realization of the new data-driven vision for RIM, and more. .
  4. Implement the desired data model, taking into account the requirements of the IDMP and any other relevant regulatory request, as well as the supporting tools or platform to enable their combination with the associated processes. Consider the possibility of expanding this capability to encompass functions beyond regulatory remit, such as quality / safety / PV and labeling management. A configurable platform will ideally support a wide range of use cases, based on the same core data set.
  5. Establish appropriate data governance, reflecting the interdependencies between roles and teams across the value chain, so that data quality can be enhanced across departments. This may require change management to support a revised / optimized organizational structure.
  6. Get started, ensuring that data governance and an associated process body are in place to maintain this valuable utility in a sustainable manner, supporting continuous improvement in data and process quality.

The earlier companies start, the less likely they are to compromise their vision.



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